Review of Prairie Time: A Blackland Portrait by Matt White

Nowadays, the Blackland Prairies of north Texas are the kind of landscape most people think of as great for subdivisions and strip malls: generally flat, easily bulldozed, and not too far from Dallas-Fort Worth. Prairie Time: A Blackland Portrait traces a similar utilitarian vision of the prairie in 19th-century pioneer descriptions as well: good for plowing, grazing, and-once the buffalo and Native Americans are exterminated-not too far from outposts of commerce. The book serves as an environmental jeremiad for a place too easily seen as useful and thus too often ignored for preservation. Matt White gives readers a context in which to begin to value the Blackland Prairie by combining a heartfelt story with a thorough sense of its ecological wonder, our post-settlement history and its environmental impact on the land, and some remarkable stories of current preservationists working to find and save remnant gems of unplowed prairie.

Active Learning to Maximize Area Under the ROC Curve

In active learning, a machine learning algorithmis given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active learning affects AUC. We examine two existing algorithms from the literature and present our own active learning algorithms designed to maximize the AUC of the hypothesis. One of our algorithms was consistently the top performer, and Closest Sampling from the literature often came in second behind it. When good posterior probability estimates were available, our heuristics were by far the best.

Who Suffers Most when Disease Outbreaks and Food Recalls Happen? The Case of Mad Cow Disease in the United States

It is generally observed that whenever there are cases of disease outbreaks and food recalls, such as the case of the 2003 Mad Cow Disease (Bovine Spongiform Encephalopathy or BSE) outbreak, cattle and beef prices fall. Given these incidents, there is the question of which part of the marketing chain is the most affected. For those who produce live cattle, such as feedlot operators, the question is ‘what effect these events have on price and demand for beef and cattle?’ Similarly, how do the Food Safety Inspection Service (FSIS) recalls and diseases such as Mad Cow Disease outbreaks affect the beef marketing margins at all levels in the U.S. beef marketing chain? Identifying these effects along the marketing chain provides insight into which level along that channel is the most vulnerable to these events. In addition, this information helps to assess the impact of such events on the industry, providing a basis for policy formulation.